



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a      :  Tuple			     =			logging.get_logger(__name__)

a      :  str			     =			{
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''bridgetower_vision_model'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=288,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=1E-05,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=False,   **SCREAMING_SNAKE_CASE_,   )       ->   Any:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     hidden_size
						UpperCAmelCase_:		Dict							      =     num_hidden_layers
						UpperCAmelCase_:		Any							      =     num_channels
						UpperCAmelCase_:		Union[str, Any]							      =     patch_size
						UpperCAmelCase_:		Optional[int]							      =     image_size
						UpperCAmelCase_:		List[Any]							      =     initializer_factor
						UpperCAmelCase_:		List[str]							      =     layer_norm_eps
						UpperCAmelCase_:		Optional[Any]							      =     stop_gradient
						UpperCAmelCase_:		Tuple							      =     share_layernorm
						UpperCAmelCase_:		Optional[Any]							      =     remove_last_layer

			@classmethod
			def      __snake_case       (cls,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   "PretrainedConfig":
						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     cls.get_config_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						if config_dict.get("""model_type"""					) == "bridgetower":
									UpperCAmelCase_:		Optional[int]							      =     config_dict["""text_config"""]

						if "model_type" in config_dict and hasattr(cls,   """model_type"""					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''bridgetower_text_model'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=50265,   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=3072,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=514,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=1E-05,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=0,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_="absolute",   SCREAMING_SNAKE_CASE_=True,   **SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						super().__init__(**SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     vocab_size
						UpperCAmelCase_:		Any							      =     hidden_size
						UpperCAmelCase_:		Tuple							      =     num_hidden_layers
						UpperCAmelCase_:		Optional[Any]							      =     num_attention_heads
						UpperCAmelCase_:		Optional[Any]							      =     hidden_act
						UpperCAmelCase_:		Dict							      =     initializer_factor
						UpperCAmelCase_:		str							      =     intermediate_size
						UpperCAmelCase_:		Any							      =     hidden_dropout_prob
						UpperCAmelCase_:		int							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		Any							      =     max_position_embeddings
						UpperCAmelCase_:		Any							      =     type_vocab_size
						UpperCAmelCase_:		str							      =     layer_norm_eps
						UpperCAmelCase_:		List[Any]							      =     position_embedding_type
						UpperCAmelCase_:		Tuple							      =     use_cache
						UpperCAmelCase_:		Tuple							      =     pad_token_id
						UpperCAmelCase_:		Tuple							      =     bos_token_id
						UpperCAmelCase_:		List[str]							      =     eos_token_id

			@classmethod
			def      __snake_case       (cls,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   "PretrainedConfig":
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     cls.get_config_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						if config_dict.get("""model_type"""					) == "bridgetower":
									UpperCAmelCase_:		List[str]							      =     config_dict["""text_config"""]

						if "model_type" in config_dict and hasattr(cls,   """model_type"""					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''bridgetower'''

			def __init__(self,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=768,   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=1E-05,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_="add",   SCREAMING_SNAKE_CASE_=12,   SCREAMING_SNAKE_CASE_=6,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=None,   **SCREAMING_SNAKE_CASE_,   )       ->   Optional[Any]:
						# TODO: remove this once the Hub files are updated.
						UpperCAmelCase_:		Optional[Any]							      =     kwargs.pop("""text_config_dict""",   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     kwargs.pop("""vision_config_dict""",   SCREAMING_SNAKE_CASE_					)

						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     share_cross_modal_transformer_layers
						UpperCAmelCase_:		int							      =     hidden_act
						UpperCAmelCase_:		List[str]							      =     hidden_size
						UpperCAmelCase_:		List[Any]							      =     initializer_factor
						UpperCAmelCase_:		List[Any]							      =     layer_norm_eps
						UpperCAmelCase_:		List[Any]							      =     share_link_tower_layers
						UpperCAmelCase_:		Union[str, Any]							      =     link_tower_type
						UpperCAmelCase_:		Optional[int]							      =     num_attention_heads
						UpperCAmelCase_:		int							      =     num_hidden_layers
						UpperCAmelCase_:		Optional[Any]							      =     tie_word_embeddings
						UpperCAmelCase_:		List[Any]							      =     init_layernorm_from_vision_encoder

						if text_config is None:
									UpperCAmelCase_:		Tuple							      =     {}
									logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""					)

						if vision_config is None:
									UpperCAmelCase_:		Tuple							      =     {}
									logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""					)

						UpperCAmelCase_:		Dict							      =     BridgeTowerTextConfig(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     BridgeTowerVisionConfig(**SCREAMING_SNAKE_CASE_					)

			@classmethod
			def      __snake_case       (cls,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						return cls(text_config=text_config.to_dict(),   vision_config=vision_config.to_dict(),   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Tuple							      =     copy.deepcopy(self.__dict__					)
						UpperCAmelCase_:		Tuple							      =     self.text_config.to_dict()
						UpperCAmelCase_:		Any							      =     self.vision_config.to_dict()
						UpperCAmelCase_:		List[Any]							      =     self.__class__.model_type
						return output











def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






